EP3304436A1 - Schnelles verfahren mit geringem speicher für bayessche inferenz, gibbs-abtastung und tiefenlernen - Google Patents
Schnelles verfahren mit geringem speicher für bayessche inferenz, gibbs-abtastung und tiefenlernenInfo
- Publication number
- EP3304436A1 EP3304436A1 EP16728149.2A EP16728149A EP3304436A1 EP 3304436 A1 EP3304436 A1 EP 3304436A1 EP 16728149 A EP16728149 A EP 16728149A EP 3304436 A1 EP3304436 A1 EP 3304436A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- distribution
- samples
- boltzmann machine
- biases
- weights
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 70
- 238000005070 sampling Methods 0.000 title claims abstract description 45
- 238000013135 deep learning Methods 0.000 title description 7
- 238000009826 distribution Methods 0.000 claims abstract description 82
- 238000012549 training Methods 0.000 claims abstract description 58
- 239000013598 vector Substances 0.000 claims abstract description 22
- 230000006870 function Effects 0.000 claims description 26
- 238000012545 processing Methods 0.000 description 13
- 238000013459 approach Methods 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 9
- 238000005192 partition Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 4
- 230000003993 interaction Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 230000005055 memory storage Effects 0.000 description 3
- 230000006855 networking Effects 0.000 description 3
- 238000009827 uniform distribution Methods 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000001815 facial effect Effects 0.000 description 1
- 238000007429 general method Methods 0.000 description 1
- 230000003278 mimic effect Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/047—Probabilistic or stochastic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/088—Non-supervised learning, e.g. competitive learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201562171195P | 2015-06-04 | 2015-06-04 | |
PCT/US2016/032942 WO2016196005A1 (en) | 2015-06-04 | 2016-05-18 | Fast low-memory methods for bayesian inference, gibbs sampling and deep learning |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3304436A1 true EP3304436A1 (de) | 2018-04-11 |
Family
ID=56116536
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP16728149.2A Pending EP3304436A1 (de) | 2015-06-04 | 2016-05-18 | Schnelles verfahren mit geringem speicher für bayessche inferenz, gibbs-abtastung und tiefenlernen |
Country Status (3)
Country | Link |
---|---|
US (1) | US20180137422A1 (de) |
EP (1) | EP3304436A1 (de) |
WO (1) | WO2016196005A1 (de) |
Families Citing this family (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2017116446A1 (en) * | 2015-12-30 | 2017-07-06 | Google Inc. | Quantum phase estimation of multiple eigenvalues |
WO2018058061A1 (en) | 2016-09-26 | 2018-03-29 | D-Wave Systems Inc. | Systems, methods and apparatus for sampling from a sampling server |
US11531852B2 (en) * | 2016-11-28 | 2022-12-20 | D-Wave Systems Inc. | Machine learning systems and methods for training with noisy labels |
US10339408B2 (en) * | 2016-12-22 | 2019-07-02 | TCL Research America Inc. | Method and device for Quasi-Gibbs structure sampling by deep permutation for person identity inference |
KR102036968B1 (ko) * | 2017-10-19 | 2019-10-25 | 한국과학기술원 | 전문화에 기반한 신뢰성 높은 딥러닝 앙상블 방법 및 장치 |
WO2019118644A1 (en) | 2017-12-14 | 2019-06-20 | D-Wave Systems Inc. | Systems and methods for collaborative filtering with variational autoencoders |
US11386346B2 (en) | 2018-07-10 | 2022-07-12 | D-Wave Systems Inc. | Systems and methods for quantum bayesian networks |
US11074519B2 (en) | 2018-09-20 | 2021-07-27 | International Business Machines Corporation | Quantum algorithm concatenation |
US10504033B1 (en) | 2018-11-13 | 2019-12-10 | Atom Computing Inc. | Scalable neutral atom based quantum computing |
US11580435B2 (en) | 2018-11-13 | 2023-02-14 | Atom Computing Inc. | Scalable neutral atom based quantum computing |
US11461644B2 (en) | 2018-11-15 | 2022-10-04 | D-Wave Systems Inc. | Systems and methods for semantic segmentation |
US11468293B2 (en) | 2018-12-14 | 2022-10-11 | D-Wave Systems Inc. | Simulating and post-processing using a generative adversarial network |
US11900264B2 (en) | 2019-02-08 | 2024-02-13 | D-Wave Systems Inc. | Systems and methods for hybrid quantum-classical computing |
US11625612B2 (en) | 2019-02-12 | 2023-04-11 | D-Wave Systems Inc. | Systems and methods for domain adaptation |
US11120359B2 (en) | 2019-03-15 | 2021-09-14 | Microsoft Technology Licensing, Llc | Phase estimation with randomized hamiltonians |
KR20220149584A (ko) | 2020-03-02 | 2022-11-08 | 아톰 컴퓨팅 인크. | 확장 가능한 중성 원자 기반 양자 컴퓨팅 |
CN111598246B (zh) * | 2020-04-22 | 2021-10-22 | 北京百度网讯科技有限公司 | 量子吉布斯态生成方法、装置及电子设备 |
US11875227B2 (en) | 2022-05-19 | 2024-01-16 | Atom Computing Inc. | Devices and methods for forming optical traps for scalable trapped atom computing |
-
2016
- 2016-05-18 WO PCT/US2016/032942 patent/WO2016196005A1/en active Application Filing
- 2016-05-18 EP EP16728149.2A patent/EP3304436A1/de active Pending
- 2016-05-18 US US15/579,190 patent/US20180137422A1/en not_active Abandoned
Also Published As
Publication number | Publication date |
---|---|
US20180137422A1 (en) | 2018-05-17 |
WO2016196005A1 (en) | 2016-12-08 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2016196005A1 (en) | Fast low-memory methods for bayesian inference, gibbs sampling and deep learning | |
Guo et al. | Accelerating large-scale inference with anisotropic vector quantization | |
Fan et al. | A selective overview of deep learning | |
US11295207B2 (en) | Quantum deep learning | |
Ji et al. | Differential privacy and machine learning: a survey and review | |
Larochelle et al. | Learning algorithms for the classification restricted Boltzmann machine | |
US10417370B2 (en) | Classical simulation constants and ordering for quantum chemistry simulation | |
Kang | Fast determinantal point process sampling with application to clustering | |
Liu et al. | A weighted Lq adaptive least squares support vector machine classifiers–Robust and sparse approximation | |
Lu et al. | Knowledge transfer in vision recognition: A survey | |
Ngairangbam et al. | Invisible Higgs search through Vector Boson Fusion: A deep learning approach | |
Chen et al. | Research on complex classification algorithm of breast cancer chip based on SVM-RFE gene feature screening | |
Dushatskiy et al. | A novel surrogate-assisted evolutionary algorithm applied to partition-based ensemble learning | |
Ferrandiz et al. | Bayesian instance selection for the nearest neighbor rule | |
Wang et al. | A pipeline for optimizing f1-measure in multi-label text classification | |
Luo et al. | Adaptive lightweight regularization tool for complex analytics | |
Mehrbani et al. | Low‐rank isomap algorithm | |
Nguyen et al. | Meta-learning and personalization layer in federated learning | |
Yao et al. | Sparse support vector machine with L p penalty for feature selection | |
Mahalakshmi et al. | Collaborative text and image based information retrieval model using bilstm and residual networks | |
Mariia et al. | A study of neural networks point source extraction on simulated Fermi/LAT telescope images | |
Zdunek et al. | Distributed geometric nonnegative matrix factorization and hierarchical alternating least squares–based nonnegative tensor factorization with the MapReduce paradigm | |
Pandey et al. | Generative Restricted Kernel Machines. | |
Simon et al. | Discriminant analysis with adaptively pooled covariance | |
Kaya et al. | eFis: A Fuzzy Inference Method for Predicting Malignancy of Small Pulmonary Nodules |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE |
|
PUAI | Public reference made under article 153(3) epc to a published international application that has entered the european phase |
Free format text: ORIGINAL CODE: 0009012 |
|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE |
|
17P | Request for examination filed |
Effective date: 20171120 |
|
AK | Designated contracting states |
Kind code of ref document: A1 Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR |
|
AX | Request for extension of the european patent |
Extension state: BA ME |
|
DAV | Request for validation of the european patent (deleted) | ||
DAX | Request for extension of the european patent (deleted) | ||
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: EXAMINATION IS IN PROGRESS |
|
17Q | First examination report despatched |
Effective date: 20210209 |
|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: EXAMINATION IS IN PROGRESS |
|
RAP3 | Party data changed (applicant data changed or rights of an application transferred) |
Owner name: MICROSOFT TECHNOLOGY LICENSING, LLC |
|
RAP3 | Party data changed (applicant data changed or rights of an application transferred) |
Owner name: MICROSOFT TECHNOLOGY LICENSING, LLC |